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Data Science – A New Career Choice for All Women

Don’t let uncertainty or unawareness stop you from giving yourself this opportunity.

Data Science is an excellent career choice for all women at any stage in life – whether they are just starting out, considering a professional transition, or thinking of re-entering the workforce. Yet too few women make that choice.

Here is why even, and especially if you have no technical skills whatsoever, you should reconsider your options.

Image sourced via Shutterstock standard license to Anastasia Ulianova
Image sourced via Shutterstock standard license to Anastasia Ulianova

Haven’t you heard? Data is the new Oil. And the world needs more people able to gather it, process it, work it, understand it, and translate it into useful insights. Thus, it comes as no surprise that the interdisciplinary field of Data Science has experienced an explosive job growth of 650% since 2012.

Yet, even with such an impressive performance, only an estimated 15–22% of data professionals are women.

There are many reasons for this, most notably of which is a persistent shortage of qualified Women across the board in the fields of Science, Technology, Engineering, and Math (STEM). But my purpose here is not to explore why women are getting the smaller piece of the pie, but rather why more women should consider digging into it in the first place.

It is no secret that women continue to face important challenges in the workforce, despite constituting a significant portion of it. The gender pay gap, discrimination, harassment – to name a few of the most pressing ones. Although more voices have been heard in recent years and steps taken to improve the situation, these issues persist across many industries and the world. As a result, the difficult decision of which career path to pursue is made even more so for women, who must additionally take the time to carefully consider the right place of work for them.

This is especially relevant in our pandemic and post-pandemic world. Indeed, over the past year society has witnessed the strong negative impact of the COVID-19 crisis on gender parity in the workforce, with some painful statistics for women as millions have been forced to leave their jobs or accept pay cuts, and many have taken on additional unpaid responsibilities at home.

Interestingly, however, another consequence of the lockdown for a great number of people, including myself, has been a reassessment of our individual goals and priorities. According to a survey conducted by MetLife, as many as 1 in 4 women have considered a career change at this time, with 2 in 5 expressing interest in pursuing a career in STEM.

Still, perception of and uncertainty towards STEM remain one of the bigger barriers for women considering such a move. I would like to blow these away, to help more women improve their lives by making this decision.

So first off, what is a Data Scientist, and what do Data Scientists do?

If you Google ‘What is Data Science?’, Wikipedia will tell you that "Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply actionable insights from data across a broad range of application domains."

Now I will stop right there – for anyone starting with no technical skills or experience, that can be an intimidating definition. So instead let me break it down:

Data Science is a very learnable and today increasingly valuable technical skillset that combines some knowledge of programming, statistics, and modeling in order to work with and extract interesting and useful insights from different types of data. Data Scientists have one mission – to find the story that the data is telling and understand what it means for their business/problem/objective.

In our data-driven world, companies are increasingly relying on data to make smarter business decisions, whether it be related to identifying what causes a business to lose or attract customers, personalizing healthcare recommendations, creating targeted advertisements, detecting fraud, or even things like finding the next group of world-class sports athletes (cough, cough Moneyball) or, for anyone who has ever used a dating app, finding love.

The examples of Data Science applications are endless. If you are still curious, check out this link on 17 Data Science Applications & Examples.

Whatever your industry, problem, interest, or passion, you can be certain there is a way to apply Data Science to it.

Are you starting to see all the possibilities waiting for you ahead?

I. Why Is Data Science a Fantastic Career Choice for Women?

The first and foremost reasons that come to mind when discussing Data Science as a positive career choice are the widely heard promises of high pay and job growth. Indeed, looking at the data on Glassdoor (put together by a Data Scientist no doubt) the national average salary for a Data Scientist in the United States is approximately $114K, compared to a general population average of $31K.

Now isn’t that promising?

As for job growth, if the increase in available jobs since 2012 hasn’t impressed upon you the growing potential of this field, then consider what LinkedIn tells us in their 2020 Emerging Jobs Report, where Data Science is ranked #3 with a growth rate of 37%, after Artificial Intelligence Specialist (74% and Data Science based) and Robotics Engineer:

"Data science is another field that has topped the Emerging Jobs list for three years running. It’s a specialty that’s continuing to grow significantly across all industries."

Extract from LinkedIn's 2020 Emerging Jobs Report.
Extract from LinkedIn’s 2020 Emerging Jobs Report.

The reason why this high demand is particularly important for women is two-fold:

(1) The first lies in the value of the skillset. As the pandemic has shown us, women face a higher likelihood of losing their jobs than men do. But this is not necessarily related to direct forms of discrimination. In many cases, it is simply because the nature of the job itself is less ‘valuable’ or less ‘essential’ than roles occupied by men.

Marketing, communications, education, retail are all today amongst the most female-dominated industries. Furthermore, a company’s administrative, HR, and secretarial roles are also more likely to be occupied by women. As a result, when the pandemic it, women in these positions or fields were amongst the first affected.

However, the high value of a technical skillset can protect women against such cuts. If you have experienced something similar, learning and switching to a Data Science position will certainly increase the protection of your job retention, whether in times of crisis or downsizing.

(2) The second reason a high demand is so important for women is that it limits the possibility of introducing the issue of gender into hiring decisions. With the number of companies looking to hire into analytical roles growing, women are significantly more likely to find a positive workplace and negotiate a competitive salary, as opposed to sticking to an unpleasant, or downright toxic environment for the sake of financial responsibilities and stability.

That is not to say that you may not face rejection when job hunting. Unfortunately, that is a reality we must all face one day or another, no matter the industry. What this does mean however is that in the event you are unhappy with your current work situation, as a skilled Data Scientist, you will not struggle nearly as much as in other fields to find another place of work more suitable to your values and needs.

Truly, many women working in Data Science seem to agree that the opportunities are the same for men as for women and that the difference in representation lies primarily with the lower number of trained women in STEM, as opposed to some form of discriminatory perceptions or practices.

With still such comparatively few numbers, many women working in the tech industry, as well as in Data Science specifically, have come together over the past few years and formed supportive communities to connect, learn from each other, share stories, and encourage more women to join the field.

For anyone interesting in switching their career from a non-tech field, this is perhaps the aspect of working in Data Science as a woman that I would like to highlight the most. With such an incredible community and so many inspirational stories, whether you are looking to network professionally, would like a mentor to grow your skills or maybe just connect personally with others sharing similar interest, or even sharing the same doubts, there are plenty of platforms, events and resources out there to support you.

Finally, as a truly interdisciplinary field, the skills learned in Data Science apply to any field, thus allowing for greater flexibility in choosing a professional industry. Maybe you always dreamed of working in fashion, or sports, or maybe what matters to you is the impact of your work, in which case there is the option of using Data Science for social good.

Whatever the case, taking the time to learn this skillset could lead to a reconnection with your passion, all while giving you a greater voice in your workplace.

II. The Field of Data Science Needs You

Of course, it is not just that Data Science is a good choice for women. As the foundation of businesses’ decision-making process, as well as the basis of Artificial Intelligence, the field of Data Science in itself is in critical need of more female representation as well.

Though algorithms learning from data may appear to produce outputs as close to ‘objective’ as possible, the reality is that they are programmed by people and trained based on historical data, which makes them extremely sensitive to bias. This is especially relevant when it comes to the data selection process.

Consider the following case – Company A decides to start using an AI algorithm in its hiring process. Thus, it trains the algorithm on its historical data to find and make decisions based on previous successful candidates. Now, what if the majority of these candidates were men? Or of a particular race? Or of a group of more privileged zip codes? The algorithm will pick up these patterns which will influence its decision-making process, thus inadvertently perpetuating discriminatory practices rather than eliminating them. Worse still, the Company may claim these decisions were justified because they were made by a machine.

One such real-life example is the late 2019 scandal of the Apple Card when customers noticed that it would grant smaller lines of credit to women than men.

Therefore, the more different perspectives on a team, the better. Of course, this point applies not only to the issue of gender but of diversity as a whole.

There are many articles available on this topic, so I won’t dwell on it long but, if you are interested, check out the following piece of Why the World Needs More Women Data Scientists.

III. Women’s Biggest Barriers and How to Overcome Them

If you have continued reading to this stage, I hope I have succeeded in sparking your interest to pursue Data Science professionally.

You may also now be experiencing some degree of self-doubt, as is common for women in this situation. Indeed, this doubt remains one of the biggest barriers still preventing or hindering many women from entering the field. But it doesn’t have to be.

The good news is -most obstacles faced by women interested in STEM (and Data Science specifically) are entirely perception-based. For once, it is not the world or society telling us that we cannot or should not do it. Instead, individual women are falling victims to the mistaken beliefs that they cannot make it in the tech industry, that they are not smart enough to code, or too old to learn, or any number of doubts rooted in their abilities and/or circumstances.

But the truth is – those are justifications of fear, and thus complete nonsense. Our brains are very powerful at making excuses for us. Everyone experienced it in one aspect of life or another – perhaps when trying to stick to a new exercise routine or a healthier diet, or perhaps when trying to learn something new.

Do not listen to that inner voice of doubt. You are absolutely smart enough. Most likely, you just don’t have the training. Yet.

It is often the case when considering STEM fields that people adhere to the misconception that one must already know how to program or have some basic level of technical skills or computer know-how prior to training to determine whether they will succeed in their field of interest. This is the first big barrier.

Yet, what most appear to forget is that even the most advanced software engineers or data scientists once started as complete beginners as well. Sure, we’ve all heard the stories of genius teenagers becoming computer experts before reaching adulthood, but under no circumstances does that imply a necessity to express interest in technology from an early age in order to build a successful career as a Data Scientist.

It’s true we cannot all be Einstein or Steve Jobs or Elon Musk, as much as we would wish it. Nonetheless, why is it that women are so quick to posit this as a zero-sum problem to convince themselves not to try and learn?

In 2016, a study from Harvard’s Women in Computer Science Advocacy Council discovered the startling conclusion that women with up to eight years of programming experience report the same level of confidence as men with zero to one year of programming experience. If that is the case for even experienced professionals, it is no wonder that complete beginners feel overwhelmed!

Once again, this does not have to be the case.

The key is finding the right resources and proper learning structure for your level. Luckily, there are countless options, many catered to complete beginners, just a quick Google search away. Online courses, educational videos, bootcamps, full-time programs, part-time programs, self-study options… The choice is yours.

Pick an option and commit. No one will expect you to immediately begin analyzing data like a pro on day one. Once you start learning, small step by small step, you will begin to accumulate knowledge and experience, you will get used to programming syntax, and best of all, with every step you move forward, your confidence will grow as well.

Additionally, just like finding the right learning resources is important, having the proper level of support at your disposal is crucial as well. Personally, I recommend finding a Data Science bootcamp if you are serious about transitioning. If you are looking for a lighter option to simply dip your toe in the water, then an online course is a good start. Support and structure are essential, especially to help face any level of self-doubt.

Of course, Data Science is a complex skillset that requires a minimum level of dedication to learn. In this regard, another barrier comes to light, another widespread perception – that coding is boring.

Like BCG stated in their study on "What’s Keeping Women Out of Data Science?", Data Science has a real image problem. The widely accepted convention depicts tech/data science culture as extremely brainy and nerd-like. But just like every previous perception addressed here, it is simply a matter of perspective.

Data Science is not a goal in itself, it is a skillset. Thus, whether you are bored or not will largely depend on how you decide to apply it. Naturally, some aspects of Data Science are more tedious than others, such as data gathering and/or cleaning, but isn’t that also true for any skillset?

Take cooking as an example – to prepare delicious meals, you must acquire the ingredients, prepare them (wash, peel, cut, dice, etc.), and have the right tools available before you can start making magic, and that can be a tedious process. Then, you must learn the recipe, try it out, but once you know it, it becomes a habit. Once that is done, the choice is yours for what to cook. Would you rather apply your skills to prepare a Mediterranean meal? Or maybe you prefer Asian cooking? Finally, many of us can cook a perfectly delicious meal without being the top chef in the industry.

That doesn’t sound too different from the Data Science process, does it not? The challenge will be to stick through the learning process, as you practice how to prepare your ingredients (your data) and what recipes (models) you can apply to them.

As the pandemic continues to highlight and accelerate changes in the labor force, it is becoming increasingly more critical for women to protect themselves. As highlighted by Bloomberg, a recent study by the McKinsey Global Institute suggests that over 100 million workers in 8 of the world’s largest economies may need to change their occupation by 2030, the majority of which are the less educated, women, ethnic minorities, and young people. As those affected will be required to ‘retrain’ for a higher-level skillset in an increasingly automated and digitized world, I am hoping that this article will help more women broaden their horizons and create new, better opportunities for themselves through a career change to Data Science.

So, ask yourself – how much do you want to improve your professional situation?

Finally, for anyone concerned about their age or who has ever doubted their abilities to learn something new, I want to part by sharing a link to the fun and inspirational story of Masako Wakamiya, a Japanese woman who taught herself to code at 81 years old so she could create a game app for senior citizens. The first time she held a computer was in her fifties. Do you still think you are too old to try?

A journey of a thousand miles begins with a single step. Let this decision be your first step.


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